Main content

Genomic Selection for Seed Oil Concentration in Bi-parental Soybean Populations Derived from Parents Carrying the DP-305423-1 Transgene for High Oleic Acid in the Seed

Show full item record

Title: Genomic Selection for Seed Oil Concentration in Bi-parental Soybean Populations Derived from Parents Carrying the DP-305423-1 Transgene for High Oleic Acid in the Seed
Author: Hemingway, Joel
Department: Department of Plant Agriculture
Program: Plant Agriculture
Advisor: Rajcan, Istvan
Abstract: Soybean (Glycine max [L.] Merrill) oil is an economically important commodity worldwide with many uses. High levels of polyunsaturated fatty acids cause oxidative instability of the oil; however, the DP-3054231-1 transgene confers elevated oleic acid concentrations resulting in oil with increased oxidative stability. The first objective of this thesis was to study the effects of the DP-305423-1 transgene on agronomic and seed traits across multiple genetic backgrounds and environments. An equal number of high oleic (HO) and normal oleic (NO) BC1F4:F6 progeny from four unique populations were grown at four locations in Southern Ontario and two in Northern Iowa. Overall, the difference in mean yield between the HO and NO progeny varied across populations and locations and the HO progeny consistently had lower mean oil concentration and greater mean protein concentration. Differences in 100-seed weight were not consistent across populations or locations. Genomic selection (GS) has been shown to be a valuable tool for performing selection on complex quantitative traits, such as seed oil concentration in soybean. The second objective of the thesis was to evaluate multiple GS models for seed oil concentration using a low-density marker panel in bi-parental, high oleic soybean populations and compare prediction accuracies of six unique training populations (TPs). Prediction accuracy was calculated as the Pearson correlation coefficient between the predicted value of an individual and the ‘true’ phenotypic value, as determined through multi location field testing. Genomic best linear unbiased predictor (GBLUP) produced the greatest predictability across all populations and training sets, compared to BayesA and BayesB, which had similar predictive ability across populations. Generally, TPs consisting of more individuals had greater predictability; however, variations were observed across populations and models. TPs consisting of individuals from a single location had greater predictability of all genotypes than training populations of equal size comprised of individuals from both locations, indicating potential influence of marker x environment effects across training environments. These results show that genomic selection using a low marker density can be a valuable tool for increasing oil concentration in biparental high oleic, low linolenic soybeans populations.
URI: http://hdl.handle.net/10214/15068
Date: 2019-02
Terms of Use: All items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.


Files in this item

Files Size Format View Description
Hemingway_Joel_201902_PhD.pdf 4.159Mb PDF View/Open Thesis document

This item appears in the following Collection(s)

Show full item record